Overview

Dataset statistics

Number of variables11
Number of observations36733
Missing cells0
Missing cells (%)0.0%
Duplicate rows2
Duplicate rows (%)< 0.1%
Total size in memory3.4 MiB
Average record size in memory96.0 B

Variable types

Numeric10
Categorical1

Alerts

Dataset has 2 (< 0.1%) duplicate rowsDuplicates
AT is highly overall correlated with NOXHigh correlation
AFDP is highly overall correlated with GTEP and 3 other fieldsHigh correlation
GTEP is highly overall correlated with AFDP and 3 other fieldsHigh correlation
TIT is highly overall correlated with AFDP and 3 other fieldsHigh correlation
TEY is highly overall correlated with AFDP and 3 other fieldsHigh correlation
CDP is highly overall correlated with AFDP and 3 other fieldsHigh correlation
NOX is highly overall correlated with ATHigh correlation

Reproduction

Analysis started2023-03-18 23:38:57.599976
Analysis finished2023-03-18 23:39:25.057481
Duration27.46 seconds
Software versionydata-profiling vv4.1.1
Download configurationconfig.json

Variables

AT
Real number (ℝ)

Distinct22523
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.712726
Minimum-6.2348
Maximum37.103
Zeros0
Zeros (%)0.0%
Negative62
Negative (%)0.2%
Memory size574.0 KiB
2023-03-18T23:39:25.211965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-6.2348
5-th percentile5.75584
Q111.781
median17.801
Q323.665
95-th percentile29.4848
Maximum37.103
Range43.3378
Interquartile range (IQR)11.884

Descriptive statistics

Standard deviation7.4474512
Coefficient of variation (CV)0.42045765
Kurtosis-0.82659994
Mean17.712726
Median Absolute Deviation (MAD)5.945
Skewness-0.043546722
Sum650641.57
Variance55.46453
MonotonicityNot monotonic
2023-03-18T23:39:25.470036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.92 8
 
< 0.1%
18.525 8
 
< 0.1%
23.969 8
 
< 0.1%
11.421 7
 
< 0.1%
18.431 7
 
< 0.1%
25.597 7
 
< 0.1%
20.72 7
 
< 0.1%
14.766 7
 
< 0.1%
16.792 7
 
< 0.1%
22.13 7
 
< 0.1%
Other values (22513) 36660
99.8%
ValueCountFrequency (%)
-6.2348 1
< 0.1%
-6.0421 1
< 0.1%
-5.9793 1
< 0.1%
-5.9031 1
< 0.1%
-5.8956 1
< 0.1%
-5.8847 1
< 0.1%
-5.82 1
< 0.1%
-5.8189 1
< 0.1%
-5.785 1
< 0.1%
-5.7711 1
< 0.1%
ValueCountFrequency (%)
37.103 1
< 0.1%
37.098 1
< 0.1%
36.264 1
< 0.1%
35.822 1
< 0.1%
35.461 1
< 0.1%
35.406 1
< 0.1%
35.395 1
< 0.1%
35.21 1
< 0.1%
35.161 1
< 0.1%
35.045 1
< 0.1%

AP
Real number (ℝ)

Distinct791
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.0702
Minimum985.85
Maximum1036.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:25.722800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum985.85
5-th percentile1003.3
Q11008.8
median1012.6
Q31017
95-th percentile1024.3
Maximum1036.6
Range50.75
Interquartile range (IQR)8.2

Descriptive statistics

Standard deviation6.463346
Coefficient of variation (CV)0.0063799588
Kurtosis0.44199332
Mean1013.0702
Median Absolute Deviation (MAD)4.1
Skewness0.19412101
Sum37213106
Variance41.774841
MonotonicityNot monotonic
2023-03-18T23:39:26.447204image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1012.1 297
 
0.8%
1010.8 288
 
0.8%
1011.9 284
 
0.8%
1011.8 284
 
0.8%
1011.1 283
 
0.8%
1012.2 281
 
0.8%
1010.9 279
 
0.8%
1012.6 276
 
0.8%
1012 276
 
0.8%
1012.7 275
 
0.7%
Other values (781) 33910
92.3%
ValueCountFrequency (%)
985.85 1
< 0.1%
986.16 1
< 0.1%
986.25 1
< 0.1%
986.41 2
< 0.1%
986.43 1
< 0.1%
986.56 1
< 0.1%
986.78 1
< 0.1%
986.87 1
< 0.1%
987.31 1
< 0.1%
987.43 1
< 0.1%
ValueCountFrequency (%)
1036.6 1
 
< 0.1%
1036.5 2
< 0.1%
1036.4 2
< 0.1%
1036.3 4
< 0.1%
1036.2 1
 
< 0.1%
1036 1
 
< 0.1%
1035.8 3
< 0.1%
1035.7 2
< 0.1%
1035.6 2
< 0.1%
1035.5 2
< 0.1%

AH
Real number (ℝ)

Distinct25708
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.867015
Minimum24.085
Maximum100.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:26.904910image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum24.085
5-th percentile50.7772
Q168.188
median80.47
Q389.376
95-th percentile97.3934
Maximum100.2
Range76.115
Interquartile range (IQR)21.188

Descriptive statistics

Standard deviation14.461355
Coefficient of variation (CV)0.18571862
Kurtosis-0.27459029
Mean77.867015
Median Absolute Deviation (MAD)10.164
Skewness-0.62803404
Sum2860289.1
Variance209.13079
MonotonicityNot monotonic
2023-03-18T23:39:27.364248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.14 46
 
0.1%
100.12 46
 
0.1%
100.16 42
 
0.1%
100.15 42
 
0.1%
100.11 38
 
0.1%
100.09 34
 
0.1%
100.13 33
 
0.1%
100.1 27
 
0.1%
100.17 27
 
0.1%
100.06 25
 
0.1%
Other values (25698) 36373
99.0%
ValueCountFrequency (%)
24.085 1
< 0.1%
24.666 1
< 0.1%
25.987 1
< 0.1%
26.615 1
< 0.1%
27.504 1
< 0.1%
29.27 1
< 0.1%
29.316 1
< 0.1%
29.434 1
< 0.1%
29.475 1
< 0.1%
29.551 1
< 0.1%
ValueCountFrequency (%)
100.2 4
 
< 0.1%
100.19 1
 
< 0.1%
100.18 5
 
< 0.1%
100.17 27
0.1%
100.16 42
0.1%
100.15 42
0.1%
100.14 46
0.1%
100.13 33
0.1%
100.12 46
0.1%
100.11 38
0.1%

AFDP
Real number (ℝ)

Distinct20495
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9255177
Minimum2.0874
Maximum7.6106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:27.822047image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.0874
5-th percentile2.6666
Q13.3556
median3.9377
Q34.3769
95-th percentile5.31142
Maximum7.6106
Range5.5232
Interquartile range (IQR)1.0213

Descriptive statistics

Standard deviation0.77393559
Coefficient of variation (CV)0.19715504
Kurtosis0.2246259
Mean3.9255177
Median Absolute Deviation (MAD)0.4949
Skewness0.38109657
Sum144196.04
Variance0.5989763
MonotonicityNot monotonic
2023-03-18T23:39:28.282688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0076 9
 
< 0.1%
3.2115 9
 
< 0.1%
3.7056 8
 
< 0.1%
4.3596 8
 
< 0.1%
3.8733 8
 
< 0.1%
3.8661 8
 
< 0.1%
4.176 8
 
< 0.1%
3.5297 8
 
< 0.1%
4.1083 8
 
< 0.1%
4.1364 8
 
< 0.1%
Other values (20485) 36651
99.8%
ValueCountFrequency (%)
2.0874 1
< 0.1%
2.0992 1
< 0.1%
2.1057 1
< 0.1%
2.1197 1
< 0.1%
2.1395 1
< 0.1%
2.1441 1
< 0.1%
2.1517 1
< 0.1%
2.1597 1
< 0.1%
2.1673 1
< 0.1%
2.185 1
< 0.1%
ValueCountFrequency (%)
7.6106 1
< 0.1%
7.5549 1
< 0.1%
7.3189 1
< 0.1%
7.2399 1
< 0.1%
6.9831 1
< 0.1%
6.9779 1
< 0.1%
6.956 1
< 0.1%
6.9312 1
< 0.1%
6.927 1
< 0.1%
6.9259 1
< 0.1%

GTEP
Real number (ℝ)

Distinct12967
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.563801
Minimum17.698
Maximum40.716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:28.721165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum17.698
5-th percentile19.251
Q123.129
median25.104
Q329.061
95-th percentile32.9
Maximum40.716
Range23.018
Interquartile range (IQR)5.932

Descriptive statistics

Standard deviation4.1959575
Coefficient of variation (CV)0.16413668
Kurtosis-0.65385274
Mean25.563801
Median Absolute Deviation (MAD)2.488
Skewness0.32902135
Sum939035.12
Variance17.606059
MonotonicityNot monotonic
2023-03-18T23:39:29.183476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.308 18
 
< 0.1%
25.463 16
 
< 0.1%
24.672 16
 
< 0.1%
25.487 15
 
< 0.1%
25.184 15
 
< 0.1%
25.106 15
 
< 0.1%
25.227 14
 
< 0.1%
25.352 14
 
< 0.1%
25.443 14
 
< 0.1%
25.006 14
 
< 0.1%
Other values (12957) 36582
99.6%
ValueCountFrequency (%)
17.698 1
< 0.1%
17.719 1
< 0.1%
17.738 1
< 0.1%
17.741 1
< 0.1%
17.761 1
< 0.1%
17.826 1
< 0.1%
17.857 2
< 0.1%
17.862 1
< 0.1%
17.878 2
< 0.1%
17.912 1
< 0.1%
ValueCountFrequency (%)
40.716 1
< 0.1%
40.106 1
< 0.1%
39.37 1
< 0.1%
38.922 1
< 0.1%
38.362 1
< 0.1%
38.171 1
< 0.1%
38.051 1
< 0.1%
37.877 1
< 0.1%
37.873 1
< 0.1%
37.864 1
< 0.1%

TIT
Real number (ℝ)

Distinct799
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1081.4281
Minimum1000.8
Maximum1100.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:29.638149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000.8
5-th percentile1047.8
Q11071.8
median1085.9
Q31097
95-th percentile1100.1
Maximum1100.9
Range100.1
Interquartile range (IQR)25.2

Descriptive statistics

Standard deviation17.536373
Coefficient of variation (CV)0.01621594
Kurtosis-0.045755299
Mean1081.4281
Median Absolute Deviation (MAD)12.9
Skewness-0.88827804
Sum39724098
Variance307.52438
MonotonicityNot monotonic
2023-03-18T23:39:30.105301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1100 2735
 
7.4%
1099.9 2158
 
5.9%
1100.1 1322
 
3.6%
1099.8 870
 
2.4%
1100.2 527
 
1.4%
1099.7 324
 
0.9%
1100.3 260
 
0.7%
1099.6 186
 
0.5%
1085.4 143
 
0.4%
1086.5 137
 
0.4%
Other values (789) 28071
76.4%
ValueCountFrequency (%)
1000.8 1
< 0.1%
1001.3 1
< 0.1%
1001.4 2
< 0.1%
1002.9 1
< 0.1%
1006.5 1
< 0.1%
1007.9 1
< 0.1%
1009 1
< 0.1%
1009.5 1
< 0.1%
1011.4 1
< 0.1%
1011.7 1
< 0.1%
ValueCountFrequency (%)
1100.9 1
 
< 0.1%
1100.8 1
 
< 0.1%
1100.7 1
 
< 0.1%
1100.6 3
 
< 0.1%
1100.5 15
 
< 0.1%
1100.4 87
 
0.2%
1100.3 260
 
0.7%
1100.2 527
 
1.4%
1100.1 1322
3.6%
1100 2735
7.4%

TAT
Real number (ℝ)

Distinct2769
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean546.15852
Minimum511.04
Maximum550.61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:30.579168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum511.04
5-th percentile529.96
Q1544.72
median549.88
Q3550.04
95-th percentile550.3
Maximum550.61
Range39.57
Interquartile range (IQR)5.32

Descriptive statistics

Standard deviation6.8423604
Coefficient of variation (CV)0.012528158
Kurtosis2.0167917
Mean546.15852
Median Absolute Deviation (MAD)0.26
Skewness-1.7559071
Sum20062041
Variance46.817896
MonotonicityNot monotonic
2023-03-18T23:39:31.085667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
550.01 657
 
1.8%
550 648
 
1.8%
549.98 639
 
1.7%
549.99 628
 
1.7%
549.96 625
 
1.7%
550.04 611
 
1.7%
549.97 607
 
1.7%
550.02 590
 
1.6%
550.03 590
 
1.6%
549.94 584
 
1.6%
Other values (2759) 30554
83.2%
ValueCountFrequency (%)
511.04 1
< 0.1%
512.45 1
< 0.1%
512.6 2
< 0.1%
513.06 1
< 0.1%
513.09 1
< 0.1%
513.17 1
< 0.1%
513.29 1
< 0.1%
513.47 1
< 0.1%
513.75 1
< 0.1%
514.3 1
< 0.1%
ValueCountFrequency (%)
550.61 1
 
< 0.1%
550.6 1
 
< 0.1%
550.59 1
 
< 0.1%
550.57 2
 
< 0.1%
550.56 3
 
< 0.1%
550.55 4
 
< 0.1%
550.54 2
 
< 0.1%
550.53 5
< 0.1%
550.52 8
< 0.1%
550.51 11
< 0.1%

TEY
Real number (ℝ)

Distinct6236
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.5064
Minimum100.02
Maximum179.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:31.607349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum100.02
5-th percentile109.03
Q1124.45
median133.73
Q3144.08
95-th percentile161.33
Maximum179.5
Range79.48
Interquartile range (IQR)19.63

Descriptive statistics

Standard deviation15.618634
Coefficient of variation (CV)0.1169879
Kurtosis-0.50019625
Mean133.5064
Median Absolute Deviation (MAD)9.76
Skewness0.11655477
Sum4904090.7
Variance243.94174
MonotonicityNot monotonic
2023-03-18T23:39:31.907757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133.78 185
 
0.5%
133.74 174
 
0.5%
133.76 168
 
0.5%
133.67 163
 
0.4%
133.79 149
 
0.4%
133.72 145
 
0.4%
133.75 141
 
0.4%
133.73 140
 
0.4%
133.77 136
 
0.4%
133.68 135
 
0.4%
Other values (6226) 35197
95.8%
ValueCountFrequency (%)
100.02 1
< 0.1%
100.03 1
< 0.1%
100.04 1
< 0.1%
100.07 1
< 0.1%
100.14 1
< 0.1%
100.17 1
< 0.1%
100.2 2
< 0.1%
100.22 1
< 0.1%
100.32 1
< 0.1%
100.36 1
< 0.1%
ValueCountFrequency (%)
179.5 1
< 0.1%
178.31 1
< 0.1%
177.91 1
< 0.1%
177.88 1
< 0.1%
177.49 1
< 0.1%
176.91 1
< 0.1%
176.71 1
< 0.1%
176.55 1
< 0.1%
176.35 1
< 0.1%
176.25 1
< 0.1%

CDP
Real number (ℝ)

Distinct4447
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.060525
Minimum9.8518
Maximum15.159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:32.188101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum9.8518
5-th percentile10.385
Q111.435
median11.965
Q312.855
95-th percentile13.989
Maximum15.159
Range5.3072
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.0887953
Coefficient of variation (CV)0.090277603
Kurtosis-0.63158758
Mean12.060525
Median Absolute Deviation (MAD)0.637
Skewness0.23679157
Sum443019.27
Variance1.1854752
MonotonicityNot monotonic
2023-03-18T23:39:32.464749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.891 55
 
0.1%
11.872 47
 
0.1%
11.908 46
 
0.1%
11.902 45
 
0.1%
11.899 44
 
0.1%
11.916 43
 
0.1%
11.901 43
 
0.1%
11.835 43
 
0.1%
11.839 43
 
0.1%
11.856 41
 
0.1%
Other values (4437) 36283
98.8%
ValueCountFrequency (%)
9.8518 1
< 0.1%
9.8708 1
< 0.1%
9.8754 1
< 0.1%
9.8806 1
< 0.1%
9.9044 1
< 0.1%
9.9046 1
< 0.1%
9.9178 1
< 0.1%
9.9239 1
< 0.1%
9.9244 1
< 0.1%
9.9286 1
< 0.1%
ValueCountFrequency (%)
15.159 1
< 0.1%
15.083 1
< 0.1%
15.081 1
< 0.1%
15.055 1
< 0.1%
15.043 1
< 0.1%
15.042 1
< 0.1%
15.039 1
< 0.1%
15.031 1
< 0.1%
15.029 1
< 0.1%
15.002 1
< 0.1%

NOX
Real number (ℝ)

Distinct23637
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.293067
Minimum25.905
Maximum119.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size574.0 KiB
2023-03-18T23:39:32.727710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25.905
5-th percentile49.2908
Q157.162
median63.849
Q371.548
95-th percentile85.189
Maximum119.91
Range94.005
Interquartile range (IQR)14.386

Descriptive statistics

Standard deviation11.678357
Coefficient of variation (CV)0.1788606
Kurtosis2.0375914
Mean65.293067
Median Absolute Deviation (MAD)7.132
Skewness1.0267789
Sum2398410.2
Variance136.38403
MonotonicityNot monotonic
2023-03-18T23:39:32.993698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.38 104
 
0.3%
64.096 9
 
< 0.1%
64.299 8
 
< 0.1%
64.109 8
 
< 0.1%
65.581 7
 
< 0.1%
64.105 7
 
< 0.1%
66.412 7
 
< 0.1%
56.77 7
 
< 0.1%
66.861 7
 
< 0.1%
61.579 7
 
< 0.1%
Other values (23627) 36562
99.5%
ValueCountFrequency (%)
25.905 1
< 0.1%
27.183 1
< 0.1%
27.765 1
< 0.1%
29.063 1
< 0.1%
35.598 1
< 0.1%
35.661 1
< 0.1%
36.676 1
< 0.1%
37.616 1
< 0.1%
38.011 1
< 0.1%
38.172 1
< 0.1%
ValueCountFrequency (%)
119.91 1
< 0.1%
119.9 1
< 0.1%
119.89 1
< 0.1%
119.79 1
< 0.1%
119.68 1
< 0.1%
119.52 1
< 0.1%
119.49 1
< 0.1%
119.48 1
< 0.1%
119.43 1
< 0.1%
119.41 1
< 0.1%

Year
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size574.0 KiB
2012
7628 
2011
7411 
2015
7384 
2014
7158 
2013
7152 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters146932
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015
2nd row2015
3rd row2015
4th row2015
5th row2015

Common Values

ValueCountFrequency (%)
2012 7628
20.8%
2011 7411
20.2%
2015 7384
20.1%
2014 7158
19.5%
2013 7152
19.5%

Length

2023-03-18T23:39:33.249786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-18T23:39:33.508885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2012 7628
20.8%
2011 7411
20.2%
2015 7384
20.1%
2014 7158
19.5%
2013 7152
19.5%

Most occurring characters

ValueCountFrequency (%)
2 44361
30.2%
1 44144
30.0%
0 36733
25.0%
5 7384
 
5.0%
4 7158
 
4.9%
3 7152
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146932
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 44361
30.2%
1 44144
30.0%
0 36733
25.0%
5 7384
 
5.0%
4 7158
 
4.9%
3 7152
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Common 146932
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 44361
30.2%
1 44144
30.0%
0 36733
25.0%
5 7384
 
5.0%
4 7158
 
4.9%
3 7152
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146932
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 44361
30.2%
1 44144
30.0%
0 36733
25.0%
5 7384
 
5.0%
4 7158
 
4.9%
3 7152
 
4.9%

Interactions

2023-03-18T23:39:22.294896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:38:59.616354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:02.763698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:04.838289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:06.975737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:09.020601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:11.476884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:14.596678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:17.323449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:20.207818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:22.479054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:38:59.955769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:02.965355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:05.034469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:07.171962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:09.214312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:11.769790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:14.933061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:17.524890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:20.394010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:22.674755image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:00.339870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:03.179184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:05.247082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:07.366663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:09.410467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:12.073494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:15.276521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:18.435897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:20.588220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:22.906568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:00.740134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:03.395007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:05.464359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:07.575250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:09.614303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:12.399166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:15.649473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:18.668858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:20.817625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:23.111812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:01.058288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:03.590254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:05.673045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:07.772826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:09.816347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:12.656447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:16.008981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:18.881430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:21.023511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:23.303939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:01.408478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:03.789643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:05.878883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:07.972825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:10.011261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:12.940398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:16.223264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:19.093399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:21.223321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:23.505775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:01.765539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:03.990985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:06.094166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:08.187712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:10.258592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:13.269121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:16.434126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:19.311375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:21.426360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:23.715689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:02.165308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:04.229915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:06.335489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:08.403515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:10.553149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:13.624211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:16.666597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:19.535807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:21.660082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:23.945590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:02.377703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:04.450816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:06.564046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:08.626493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:10.870551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:13.964260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:16.892656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:19.774255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:21.890750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:24.146765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:02.576179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:04.645365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:06.777019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:08.824947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:11.170352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:14.281252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:17.114788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:19.996599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-18T23:39:22.095374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-03-18T23:39:33.669209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ATAPAHAFDPGTEPTITTATTEYCDPNOXYear
AT1.000-0.410-0.4490.2970.1550.1790.187-0.0570.130-0.5800.092
AP-0.4101.000-0.031-0.065-0.0110.002-0.1430.0770.0420.1940.102
AH-0.449-0.0311.000-0.166-0.281-0.2450.054-0.143-0.2500.1500.178
AFDP0.297-0.065-0.1661.0000.6960.742-0.3440.6550.726-0.1400.207
GTEP0.155-0.011-0.2810.6961.0000.926-0.4820.9210.968-0.1540.170
TIT0.1790.002-0.2450.7420.9261.000-0.4500.9290.963-0.0780.240
TAT0.187-0.1430.054-0.344-0.482-0.4501.000-0.499-0.491-0.0850.137
TEY-0.0570.077-0.1430.6550.9210.929-0.4991.0000.950-0.0040.242
CDP0.1300.042-0.2500.7260.9680.963-0.4910.9501.000-0.1080.185
NOX-0.5800.1940.150-0.140-0.154-0.078-0.085-0.004-0.1081.0000.228
Year0.0920.1020.1780.2070.1700.2400.1370.2420.1850.2281.000

Missing values

2023-03-18T23:39:24.445114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-18T23:39:24.821149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ATAPAHAFDPGTEPTITTATTEYCDPNOXYear
01.953201020.184.9852.530420.1161048.7544.92116.2710.799113.2502015
11.219101020.187.5232.393718.5841045.5548.50109.1810.347112.0202015
20.949151022.278.3352.778922.2641068.8549.95125.8811.25688.1472015
31.007501021.776.9422.817023.3581075.2549.63132.2111.70287.0782015
41.285801021.676.7322.837723.4831076.2549.68133.5811.73782.5152015
51.831901021.776.4112.841023.4951076.4549.92133.5811.82981.1932015
62.074001022.075.9742.798122.9451073.7549.98131.5311.68783.1712015
71.782401022.673.5352.832723.3371075.7550.01133.1811.74585.7492015
81.593001023.272.8732.872923.6541078.5550.06135.3811.77286.4912015
91.681901023.872.4412.905823.4631077.9550.12134.8611.74286.3282015
ATAPAHAFDPGTEPTITTATTEYCDPNOXYear
3672310.45401004.598.3883.555518.9371053.4550.03110.7810.32779.1892011
3672410.30501004.699.2823.533918.9091053.3550.00110.7810.32879.1652011
3672510.23801004.699.9953.880521.2061067.5550.32121.2611.00276.9852011
3672610.34701004.9100.1704.319824.0481084.3549.98133.7411.68574.5472011
3672710.15501005.199.9853.704319.8371059.7549.90115.5210.57078.2352011
367289.03011005.698.4603.542119.1641049.7546.21111.6110.40079.5592011
367297.88791005.999.0933.505919.4141046.3543.22111.7810.43379.9172011
367307.26471006.399.4963.477019.5301037.7537.32110.1910.48390.9122011
367317.00601006.899.0083.448619.3771043.2541.24110.7410.53393.2272011
367326.92791007.297.5333.427519.3061049.9545.85111.5810.58392.4982011

Duplicate rows

Most frequently occurring

ATAPAHAFDPGTEPTITTATTEYCDPNOXYear# duplicates
023.1561004.295.9384.054724.6721076.6549.87127.0111.83547.35220145
126.0671008.387.3285.070329.9841099.1546.78146.1413.03852.56420144